1 Data preparation

1.1 Outline

  • Load scripts: loads libraries and useful scripts used in the analyses; all .R files contained in scripts at the root of the factory are automatically loaded

  • Load data: imports datasets, and may contain some ad hoc changes to the data such as specific data cleaning (not used in other reports), new variables used in the analyses, etc.

1.2 Load packages


library(reportfactory)
library(here)
library(rio) 
library(tidyverse)
library(incidence)
library(distcrete)
library(epitrix)
library(earlyR)
library(projections)
library(linelist)
library(remotes)
library(janitor)
library(kableExtra)
library(DT)
library(cyphr)
library(chngpt)
library(lubridate)
library(ggpubr)
library(ggnewscale)

1.3 Load scripts

These scripts will load:

  • all scripts stored as .R files inside /scripts/
  • all scripts stored as .R files inside /src/

These scripts also contain routines to access the latest clean encrypted data (see next section).


reportfactory::rfh_load_scripts()

1.4 Load clean data

We import the latest NHS pathways data:


x <- import_pathways() %>%
  as_tibble()
x
## # A tibble: 133,902 x 11
##    site_type date       sex   age   ccg_code ccg_name count postcode nhs_region
##    <chr>     <date>     <chr> <chr> <chr>    <chr>    <int> <chr>    <chr>     
##  1 111       2020-03-18 fema… 0-18  e380000… nhs_bar…    35 rm13ae   London    
##  2 111       2020-03-18 fema… 0-18  e380000… nhs_bed…    27 mk454hr  East of E…
##  3 111       2020-03-18 fema… 0-18  e380000… nhs_bla…     9 bb12fd   North West
##  4 111       2020-03-18 fema… 0-18  e380000… nhs_bro…    11 br33ql   London    
##  5 111       2020-03-18 fema… 0-18  e380000… nhs_can…     9 ws111jp  Midlands  
##  6 111       2020-03-18 fema… 0-18  e380000… nhs_cit…    12 n15lz    London    
##  7 111       2020-03-18 fema… 0-18  e380000… nhs_enf…     7 en40dy   London    
##  8 111       2020-03-18 fema… 0-18  e380000… nhs_ham…     6 dl62uu   North Eas…
##  9 111       2020-03-18 fema… 0-18  e380000… nhs_har…    24 ts232la  North Eas…
## 10 111       2020-03-18 fema… 0-18  e380000… nhs_kin…     6 kt11eu   London    
## # … with 133,892 more rows, and 2 more variables: day <int>, weekday <fct>

We also import demographics data for NHS regions in England, used later in our analysis:


path <- here::here("data", "csv", "nhs_region_population_2018.csv")
nhs_region_pop <- rio::import(path) %>%
  mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))

nhs_region_pop$nhs_region <- gsub(" Of ", " of ", nhs_region_pop$nhs_region)
nhs_region_pop$nhs_region <- gsub(" And ", " and ", nhs_region_pop$nhs_region)
nhs_region_pop
##                  nhs_region variable      value
## 1                North West     0-18 0.22538599
## 2  North East and Yorkshire     0-18 0.21876449
## 3                  Midlands     0-18 0.22564656
## 4           East of England     0-18 0.22810783
## 5                    London     0-18 0.23764782
## 6                South East     0-18 0.22458811
## 7                South West     0-18 0.20799797
## 8                North West    19-69 0.64274078
## 9  North East and Yorkshire    19-69 0.64437753
## 10                 Midlands    19-69 0.63876675
## 11          East of England    19-69 0.63034229
## 12                   London    19-69 0.67820084
## 13               South East    19-69 0.63267336
## 14               South West    19-69 0.63176131
## 15               North West   70-120 0.13187323
## 16 North East and Yorkshire   70-120 0.13685797
## 17                 Midlands   70-120 0.13558669
## 18          East of England   70-120 0.14154988
## 19                   London   70-120 0.08415135
## 20               South East   70-120 0.14273853
## 21               South West   70-120 0.16024072

Finally, we import publically available deaths per NHS region:


dth <- import_deaths() %>%
  mutate(nhs_region = str_to_title(gsub("_"," ",nhs_region)))

#truncation to account for reporting delay
delay_max <- 21

dth$nhs_region <- gsub(" Of ", " of ", dth$nhs_region)
dth$nhs_region <- gsub(" And ", " and ", dth$nhs_region)
dth
##     date_report               nhs_region deaths
## 1    2020-03-01          East of England      0
## 2    2020-03-02          East of England      1
## 3    2020-03-03          East of England      0
## 4    2020-03-04          East of England      0
## 5    2020-03-05          East of England      0
## 6    2020-03-06          East of England      1
## 7    2020-03-07          East of England      0
## 8    2020-03-08          East of England      0
## 9    2020-03-09          East of England      1
## 10   2020-03-10          East of England      0
## 11   2020-03-11          East of England      0
## 12   2020-03-12          East of England      0
## 13   2020-03-13          East of England      1
## 14   2020-03-14          East of England      2
## 15   2020-03-15          East of England      2
## 16   2020-03-16          East of England      1
## 17   2020-03-17          East of England      1
## 18   2020-03-18          East of England      5
## 19   2020-03-19          East of England      4
## 20   2020-03-20          East of England      2
## 21   2020-03-21          East of England     11
## 22   2020-03-22          East of England     12
## 23   2020-03-23          East of England     11
## 24   2020-03-24          East of England     19
## 25   2020-03-25          East of England     26
## 26   2020-03-26          East of England     36
## 27   2020-03-27          East of England     38
## 28   2020-03-28          East of England     28
## 29   2020-03-29          East of England     43
## 30   2020-03-30          East of England     45
## 31   2020-03-31          East of England     70
## 32   2020-04-01          East of England     61
## 33   2020-04-02          East of England     64
## 34   2020-04-03          East of England     80
## 35   2020-04-04          East of England     71
## 36   2020-04-05          East of England     76
## 37   2020-04-06          East of England     71
## 38   2020-04-07          East of England     93
## 39   2020-04-08          East of England    111
## 40   2020-04-09          East of England     87
## 41   2020-04-10          East of England     74
## 42   2020-04-11          East of England     91
## 43   2020-04-12          East of England    101
## 44   2020-04-13          East of England     78
## 45   2020-04-14          East of England     61
## 46   2020-04-15          East of England     82
## 47   2020-04-16          East of England     74
## 48   2020-04-17          East of England     86
## 49   2020-04-18          East of England     64
## 50   2020-04-19          East of England     67
## 51   2020-04-20          East of England     67
## 52   2020-04-21          East of England     75
## 53   2020-04-22          East of England     67
## 54   2020-04-23          East of England     49
## 55   2020-04-24          East of England     66
## 56   2020-04-25          East of England     54
## 57   2020-04-26          East of England     48
## 58   2020-04-27          East of England     46
## 59   2020-04-28          East of England     58
## 60   2020-04-29          East of England     32
## 61   2020-04-30          East of England     44
## 62   2020-05-01          East of England     49
## 63   2020-05-02          East of England     29
## 64   2020-05-03          East of England     41
## 65   2020-05-04          East of England     19
## 66   2020-05-05          East of England     35
## 67   2020-05-06          East of England     30
## 68   2020-05-07          East of England     33
## 69   2020-05-08          East of England     33
## 70   2020-05-09          East of England     29
## 71   2020-05-10          East of England     22
## 72   2020-05-11          East of England     18
## 73   2020-05-12          East of England     21
## 74   2020-05-13          East of England     27
## 75   2020-05-14          East of England     26
## 76   2020-05-15          East of England     19
## 77   2020-05-16          East of England     26
## 78   2020-05-17          East of England     17
## 79   2020-05-18          East of England     24
## 80   2020-05-19          East of England     15
## 81   2020-05-20          East of England     26
## 82   2020-05-21          East of England     21
## 83   2020-05-22          East of England     13
## 84   2020-05-23          East of England     12
## 85   2020-05-24          East of England     16
## 86   2020-05-25          East of England     25
## 87   2020-05-26          East of England     14
## 88   2020-05-27          East of England     12
## 89   2020-05-28          East of England     17
## 90   2020-05-29          East of England     15
## 91   2020-05-30          East of England      9
## 92   2020-05-31          East of England      8
## 93   2020-06-01          East of England     14
## 94   2020-06-02          East of England     12
## 95   2020-06-03          East of England      7
## 96   2020-06-04          East of England      4
## 97   2020-06-05          East of England      1
## 98   2020-03-01                   London      0
## 99   2020-03-02                   London      0
## 100  2020-03-03                   London      0
## 101  2020-03-04                   London      0
## 102  2020-03-05                   London      0
## 103  2020-03-06                   London      1
## 104  2020-03-07                   London      1
## 105  2020-03-08                   London      0
## 106  2020-03-09                   London      1
## 107  2020-03-10                   London      0
## 108  2020-03-11                   London      7
## 109  2020-03-12                   London      6
## 110  2020-03-13                   London     10
## 111  2020-03-14                   London     14
## 112  2020-03-15                   London     10
## 113  2020-03-16                   London     17
## 114  2020-03-17                   London     25
## 115  2020-03-18                   London     31
## 116  2020-03-19                   London     25
## 117  2020-03-20                   London     45
## 118  2020-03-21                   London     50
## 119  2020-03-22                   London     54
## 120  2020-03-23                   London     64
## 121  2020-03-24                   London     87
## 122  2020-03-25                   London    113
## 123  2020-03-26                   London    130
## 124  2020-03-27                   London    130
## 125  2020-03-28                   London    122
## 126  2020-03-29                   London    147
## 127  2020-03-30                   London    150
## 128  2020-03-31                   London    181
## 129  2020-04-01                   London    202
## 130  2020-04-02                   London    190
## 131  2020-04-03                   London    196
## 132  2020-04-04                   London    230
## 133  2020-04-05                   London    195
## 134  2020-04-06                   London    198
## 135  2020-04-07                   London    219
## 136  2020-04-08                   London    238
## 137  2020-04-09                   London    205
## 138  2020-04-10                   London    170
## 139  2020-04-11                   London    176
## 140  2020-04-12                   London    158
## 141  2020-04-13                   London    166
## 142  2020-04-14                   London    143
## 143  2020-04-15                   London    142
## 144  2020-04-16                   London    139
## 145  2020-04-17                   London     99
## 146  2020-04-18                   London    101
## 147  2020-04-19                   London    102
## 148  2020-04-20                   London     95
## 149  2020-04-21                   London     95
## 150  2020-04-22                   London    108
## 151  2020-04-23                   London     77
## 152  2020-04-24                   London     71
## 153  2020-04-25                   London     57
## 154  2020-04-26                   London     53
## 155  2020-04-27                   London     51
## 156  2020-04-28                   London     43
## 157  2020-04-29                   London     44
## 158  2020-04-30                   London     39
## 159  2020-05-01                   London     41
## 160  2020-05-02                   London     40
## 161  2020-05-03                   London     36
## 162  2020-05-04                   London     29
## 163  2020-05-05                   London     25
## 164  2020-05-06                   London     36
## 165  2020-05-07                   London     37
## 166  2020-05-08                   London     29
## 167  2020-05-09                   London     23
## 168  2020-05-10                   London     26
## 169  2020-05-11                   London     18
## 170  2020-05-12                   London     18
## 171  2020-05-13                   London     16
## 172  2020-05-14                   London     20
## 173  2020-05-15                   London     18
## 174  2020-05-16                   London     14
## 175  2020-05-17                   London     15
## 176  2020-05-18                   London      9
## 177  2020-05-19                   London     13
## 178  2020-05-20                   London     19
## 179  2020-05-21                   London     12
## 180  2020-05-22                   London     10
## 181  2020-05-23                   London      6
## 182  2020-05-24                   London      7
## 183  2020-05-25                   London      8
## 184  2020-05-26                   London     12
## 185  2020-05-27                   London      7
## 186  2020-05-28                   London      7
## 187  2020-05-29                   London      7
## 188  2020-05-30                   London     12
## 189  2020-05-31                   London      6
## 190  2020-06-01                   London      8
## 191  2020-06-02                   London      6
## 192  2020-06-03                   London      5
## 193  2020-06-04                   London      4
## 194  2020-06-05                   London      0
## 195  2020-03-01                 Midlands      0
## 196  2020-03-02                 Midlands      0
## 197  2020-03-03                 Midlands      1
## 198  2020-03-04                 Midlands      0
## 199  2020-03-05                 Midlands      0
## 200  2020-03-06                 Midlands      0
## 201  2020-03-07                 Midlands      0
## 202  2020-03-08                 Midlands      3
## 203  2020-03-09                 Midlands      1
## 204  2020-03-10                 Midlands      0
## 205  2020-03-11                 Midlands      2
## 206  2020-03-12                 Midlands      6
## 207  2020-03-13                 Midlands      5
## 208  2020-03-14                 Midlands      4
## 209  2020-03-15                 Midlands      5
## 210  2020-03-16                 Midlands     11
## 211  2020-03-17                 Midlands      8
## 212  2020-03-18                 Midlands     13
## 213  2020-03-19                 Midlands      8
## 214  2020-03-20                 Midlands     28
## 215  2020-03-21                 Midlands     13
## 216  2020-03-22                 Midlands     31
## 217  2020-03-23                 Midlands     33
## 218  2020-03-24                 Midlands     41
## 219  2020-03-25                 Midlands     48
## 220  2020-03-26                 Midlands     64
## 221  2020-03-27                 Midlands     72
## 222  2020-03-28                 Midlands     89
## 223  2020-03-29                 Midlands     92
## 224  2020-03-30                 Midlands     90
## 225  2020-03-31                 Midlands    123
## 226  2020-04-01                 Midlands    140
## 227  2020-04-02                 Midlands    142
## 228  2020-04-03                 Midlands    124
## 229  2020-04-04                 Midlands    151
## 230  2020-04-05                 Midlands    164
## 231  2020-04-06                 Midlands    140
## 232  2020-04-07                 Midlands    123
## 233  2020-04-08                 Midlands    186
## 234  2020-04-09                 Midlands    139
## 235  2020-04-10                 Midlands    127
## 236  2020-04-11                 Midlands    142
## 237  2020-04-12                 Midlands    139
## 238  2020-04-13                 Midlands    120
## 239  2020-04-14                 Midlands    116
## 240  2020-04-15                 Midlands    147
## 241  2020-04-16                 Midlands    102
## 242  2020-04-17                 Midlands    118
## 243  2020-04-18                 Midlands    115
## 244  2020-04-19                 Midlands     92
## 245  2020-04-20                 Midlands    107
## 246  2020-04-21                 Midlands     86
## 247  2020-04-22                 Midlands     78
## 248  2020-04-23                 Midlands    103
## 249  2020-04-24                 Midlands     79
## 250  2020-04-25                 Midlands     72
## 251  2020-04-26                 Midlands     81
## 252  2020-04-27                 Midlands     74
## 253  2020-04-28                 Midlands     68
## 254  2020-04-29                 Midlands     53
## 255  2020-04-30                 Midlands     56
## 256  2020-05-01                 Midlands     64
## 257  2020-05-02                 Midlands     51
## 258  2020-05-03                 Midlands     52
## 259  2020-05-04                 Midlands     61
## 260  2020-05-05                 Midlands     58
## 261  2020-05-06                 Midlands     59
## 262  2020-05-07                 Midlands     48
## 263  2020-05-08                 Midlands     34
## 264  2020-05-09                 Midlands     37
## 265  2020-05-10                 Midlands     41
## 266  2020-05-11                 Midlands     33
## 267  2020-05-12                 Midlands     45
## 268  2020-05-13                 Midlands     39
## 269  2020-05-14                 Midlands     36
## 270  2020-05-15                 Midlands     40
## 271  2020-05-16                 Midlands     34
## 272  2020-05-17                 Midlands     31
## 273  2020-05-18                 Midlands     34
## 274  2020-05-19                 Midlands     34
## 275  2020-05-20                 Midlands     36
## 276  2020-05-21                 Midlands     32
## 277  2020-05-22                 Midlands     26
## 278  2020-05-23                 Midlands     31
## 279  2020-05-24                 Midlands     19
## 280  2020-05-25                 Midlands     24
## 281  2020-05-26                 Midlands     31
## 282  2020-05-27                 Midlands     28
## 283  2020-05-28                 Midlands     26
## 284  2020-05-29                 Midlands     20
## 285  2020-05-30                 Midlands     19
## 286  2020-05-31                 Midlands     20
## 287  2020-06-01                 Midlands     19
## 288  2020-06-02                 Midlands     20
## 289  2020-06-03                 Midlands     21
## 290  2020-06-04                 Midlands      9
## 291  2020-06-05                 Midlands      2
## 292  2020-03-01 North East and Yorkshire      0
## 293  2020-03-02 North East and Yorkshire      0
## 294  2020-03-03 North East and Yorkshire      0
## 295  2020-03-04 North East and Yorkshire      0
## 296  2020-03-05 North East and Yorkshire      0
## 297  2020-03-06 North East and Yorkshire      0
## 298  2020-03-07 North East and Yorkshire      0
## 299  2020-03-08 North East and Yorkshire      0
## 300  2020-03-09 North East and Yorkshire      0
## 301  2020-03-10 North East and Yorkshire      0
## 302  2020-03-11 North East and Yorkshire      0
## 303  2020-03-12 North East and Yorkshire      0
## 304  2020-03-13 North East and Yorkshire      0
## 305  2020-03-14 North East and Yorkshire      0
## 306  2020-03-15 North East and Yorkshire      2
## 307  2020-03-16 North East and Yorkshire      3
## 308  2020-03-17 North East and Yorkshire      1
## 309  2020-03-18 North East and Yorkshire      2
## 310  2020-03-19 North East and Yorkshire      6
## 311  2020-03-20 North East and Yorkshire      5
## 312  2020-03-21 North East and Yorkshire      6
## 313  2020-03-22 North East and Yorkshire      7
## 314  2020-03-23 North East and Yorkshire      9
## 315  2020-03-24 North East and Yorkshire      8
## 316  2020-03-25 North East and Yorkshire     18
## 317  2020-03-26 North East and Yorkshire     21
## 318  2020-03-27 North East and Yorkshire     28
## 319  2020-03-28 North East and Yorkshire     35
## 320  2020-03-29 North East and Yorkshire     38
## 321  2020-03-30 North East and Yorkshire     64
## 322  2020-03-31 North East and Yorkshire     60
## 323  2020-04-01 North East and Yorkshire     67
## 324  2020-04-02 North East and Yorkshire     74
## 325  2020-04-03 North East and Yorkshire    100
## 326  2020-04-04 North East and Yorkshire    105
## 327  2020-04-05 North East and Yorkshire     92
## 328  2020-04-06 North East and Yorkshire     96
## 329  2020-04-07 North East and Yorkshire    102
## 330  2020-04-08 North East and Yorkshire    107
## 331  2020-04-09 North East and Yorkshire    111
## 332  2020-04-10 North East and Yorkshire    117
## 333  2020-04-11 North East and Yorkshire     98
## 334  2020-04-12 North East and Yorkshire     84
## 335  2020-04-13 North East and Yorkshire     94
## 336  2020-04-14 North East and Yorkshire    107
## 337  2020-04-15 North East and Yorkshire     96
## 338  2020-04-16 North East and Yorkshire    103
## 339  2020-04-17 North East and Yorkshire     88
## 340  2020-04-18 North East and Yorkshire     95
## 341  2020-04-19 North East and Yorkshire     88
## 342  2020-04-20 North East and Yorkshire    100
## 343  2020-04-21 North East and Yorkshire     76
## 344  2020-04-22 North East and Yorkshire     84
## 345  2020-04-23 North East and Yorkshire     62
## 346  2020-04-24 North East and Yorkshire     72
## 347  2020-04-25 North East and Yorkshire     69
## 348  2020-04-26 North East and Yorkshire     65
## 349  2020-04-27 North East and Yorkshire     65
## 350  2020-04-28 North East and Yorkshire     57
## 351  2020-04-29 North East and Yorkshire     69
## 352  2020-04-30 North East and Yorkshire     57
## 353  2020-05-01 North East and Yorkshire     64
## 354  2020-05-02 North East and Yorkshire     48
## 355  2020-05-03 North East and Yorkshire     40
## 356  2020-05-04 North East and Yorkshire     49
## 357  2020-05-05 North East and Yorkshire     40
## 358  2020-05-06 North East and Yorkshire     50
## 359  2020-05-07 North East and Yorkshire     45
## 360  2020-05-08 North East and Yorkshire     42
## 361  2020-05-09 North East and Yorkshire     44
## 362  2020-05-10 North East and Yorkshire     40
## 363  2020-05-11 North East and Yorkshire     29
## 364  2020-05-12 North East and Yorkshire     27
## 365  2020-05-13 North East and Yorkshire     28
## 366  2020-05-14 North East and Yorkshire     30
## 367  2020-05-15 North East and Yorkshire     32
## 368  2020-05-16 North East and Yorkshire     35
## 369  2020-05-17 North East and Yorkshire     26
## 370  2020-05-18 North East and Yorkshire     29
## 371  2020-05-19 North East and Yorkshire     27
## 372  2020-05-20 North East and Yorkshire     21
## 373  2020-05-21 North East and Yorkshire     33
## 374  2020-05-22 North East and Yorkshire     22
## 375  2020-05-23 North East and Yorkshire     18
## 376  2020-05-24 North East and Yorkshire     23
## 377  2020-05-25 North East and Yorkshire     21
## 378  2020-05-26 North East and Yorkshire     21
## 379  2020-05-27 North East and Yorkshire     18
## 380  2020-05-28 North East and Yorkshire     19
## 381  2020-05-29 North East and Yorkshire     24
## 382  2020-05-30 North East and Yorkshire     19
## 383  2020-05-31 North East and Yorkshire     18
## 384  2020-06-01 North East and Yorkshire     15
## 385  2020-06-02 North East and Yorkshire     22
## 386  2020-06-03 North East and Yorkshire     21
## 387  2020-06-04 North East and Yorkshire     15
## 388  2020-06-05 North East and Yorkshire      7
## 389  2020-03-01               North West      0
## 390  2020-03-02               North West      0
## 391  2020-03-03               North West      0
## 392  2020-03-04               North West      0
## 393  2020-03-05               North West      1
## 394  2020-03-06               North West      0
## 395  2020-03-07               North West      0
## 396  2020-03-08               North West      1
## 397  2020-03-09               North West      0
## 398  2020-03-10               North West      0
## 399  2020-03-11               North West      0
## 400  2020-03-12               North West      2
## 401  2020-03-13               North West      3
## 402  2020-03-14               North West      1
## 403  2020-03-15               North West      4
## 404  2020-03-16               North West      2
## 405  2020-03-17               North West      4
## 406  2020-03-18               North West      6
## 407  2020-03-19               North West      7
## 408  2020-03-20               North West     10
## 409  2020-03-21               North West     11
## 410  2020-03-22               North West     13
## 411  2020-03-23               North West     16
## 412  2020-03-24               North West     21
## 413  2020-03-25               North West     21
## 414  2020-03-26               North West     29
## 415  2020-03-27               North West     35
## 416  2020-03-28               North West     28
## 417  2020-03-29               North West     46
## 418  2020-03-30               North West     67
## 419  2020-03-31               North West     52
## 420  2020-04-01               North West     86
## 421  2020-04-02               North West     96
## 422  2020-04-03               North West     95
## 423  2020-04-04               North West     98
## 424  2020-04-05               North West    102
## 425  2020-04-06               North West    100
## 426  2020-04-07               North West    133
## 427  2020-04-08               North West    127
## 428  2020-04-09               North West    119
## 429  2020-04-10               North West    117
## 430  2020-04-11               North West    138
## 431  2020-04-12               North West    126
## 432  2020-04-13               North West    127
## 433  2020-04-14               North West    131
## 434  2020-04-15               North West    114
## 435  2020-04-16               North West    134
## 436  2020-04-17               North West     97
## 437  2020-04-18               North West    113
## 438  2020-04-19               North West     71
## 439  2020-04-20               North West     83
## 440  2020-04-21               North West     76
## 441  2020-04-22               North West     86
## 442  2020-04-23               North West     85
## 443  2020-04-24               North West     66
## 444  2020-04-25               North West     65
## 445  2020-04-26               North West     55
## 446  2020-04-27               North West     54
## 447  2020-04-28               North West     57
## 448  2020-04-29               North West     62
## 449  2020-04-30               North West     59
## 450  2020-05-01               North West     44
## 451  2020-05-02               North West     56
## 452  2020-05-03               North West     55
## 453  2020-05-04               North West     48
## 454  2020-05-05               North West     48
## 455  2020-05-06               North West     44
## 456  2020-05-07               North West     49
## 457  2020-05-08               North West     42
## 458  2020-05-09               North West     30
## 459  2020-05-10               North West     41
## 460  2020-05-11               North West     34
## 461  2020-05-12               North West     38
## 462  2020-05-13               North West     24
## 463  2020-05-14               North West     26
## 464  2020-05-15               North West     33
## 465  2020-05-16               North West     32
## 466  2020-05-17               North West     24
## 467  2020-05-18               North West     30
## 468  2020-05-19               North West     34
## 469  2020-05-20               North West     25
## 470  2020-05-21               North West     25
## 471  2020-05-22               North West     26
## 472  2020-05-23               North West     30
## 473  2020-05-24               North West     26
## 474  2020-05-25               North West     31
## 475  2020-05-26               North West     27
## 476  2020-05-27               North West     27
## 477  2020-05-28               North West     27
## 478  2020-05-29               North West     19
## 479  2020-05-30               North West     17
## 480  2020-05-31               North West     13
## 481  2020-06-01               North West     12
## 482  2020-06-02               North West     25
## 483  2020-06-03               North West     14
## 484  2020-06-04               North West     14
## 485  2020-06-05               North West      5
## 486  2020-03-01               South East      0
## 487  2020-03-02               South East      0
## 488  2020-03-03               South East      1
## 489  2020-03-04               South East      0
## 490  2020-03-05               South East      1
## 491  2020-03-06               South East      0
## 492  2020-03-07               South East      0
## 493  2020-03-08               South East      1
## 494  2020-03-09               South East      1
## 495  2020-03-10               South East      1
## 496  2020-03-11               South East      1
## 497  2020-03-12               South East      0
## 498  2020-03-13               South East      1
## 499  2020-03-14               South East      1
## 500  2020-03-15               South East      5
## 501  2020-03-16               South East      8
## 502  2020-03-17               South East      7
## 503  2020-03-18               South East     10
## 504  2020-03-19               South East      9
## 505  2020-03-20               South East     14
## 506  2020-03-21               South East      7
## 507  2020-03-22               South East     25
## 508  2020-03-23               South East     20
## 509  2020-03-24               South East     22
## 510  2020-03-25               South East     29
## 511  2020-03-26               South East     34
## 512  2020-03-27               South East     34
## 513  2020-03-28               South East     36
## 514  2020-03-29               South East     54
## 515  2020-03-30               South East     58
## 516  2020-03-31               South East     65
## 517  2020-04-01               South East     65
## 518  2020-04-02               South East     55
## 519  2020-04-03               South East     72
## 520  2020-04-04               South East     80
## 521  2020-04-05               South East     82
## 522  2020-04-06               South East     88
## 523  2020-04-07               South East    100
## 524  2020-04-08               South East     83
## 525  2020-04-09               South East    104
## 526  2020-04-10               South East     88
## 527  2020-04-11               South East     88
## 528  2020-04-12               South East     88
## 529  2020-04-13               South East     84
## 530  2020-04-14               South East     65
## 531  2020-04-15               South East     72
## 532  2020-04-16               South East     56
## 533  2020-04-17               South East     86
## 534  2020-04-18               South East     57
## 535  2020-04-19               South East     70
## 536  2020-04-20               South East     85
## 537  2020-04-21               South East     50
## 538  2020-04-22               South East     54
## 539  2020-04-23               South East     57
## 540  2020-04-24               South East     64
## 541  2020-04-25               South East     51
## 542  2020-04-26               South East     51
## 543  2020-04-27               South East     40
## 544  2020-04-28               South East     40
## 545  2020-04-29               South East     47
## 546  2020-04-30               South East     29
## 547  2020-05-01               South East     37
## 548  2020-05-02               South East     36
## 549  2020-05-03               South East     17
## 550  2020-05-04               South East     35
## 551  2020-05-05               South East     29
## 552  2020-05-06               South East     25
## 553  2020-05-07               South East     27
## 554  2020-05-08               South East     26
## 555  2020-05-09               South East     28
## 556  2020-05-10               South East     19
## 557  2020-05-11               South East     25
## 558  2020-05-12               South East     27
## 559  2020-05-13               South East     18
## 560  2020-05-14               South East     32
## 561  2020-05-15               South East     24
## 562  2020-05-16               South East     22
## 563  2020-05-17               South East     17
## 564  2020-05-18               South East     22
## 565  2020-05-19               South East     12
## 566  2020-05-20               South East     22
## 567  2020-05-21               South East     14
## 568  2020-05-22               South East     17
## 569  2020-05-23               South East     19
## 570  2020-05-24               South East     16
## 571  2020-05-25               South East     13
## 572  2020-05-26               South East     17
## 573  2020-05-27               South East     17
## 574  2020-05-28               South East     12
## 575  2020-05-29               South East     17
## 576  2020-05-30               South East      8
## 577  2020-05-31               South East     10
## 578  2020-06-01               South East     11
## 579  2020-06-02               South East      9
## 580  2020-06-03               South East     12
## 581  2020-06-04               South East      6
## 582  2020-06-05               South East      2
## 583  2020-03-01               South West      0
## 584  2020-03-02               South West      0
## 585  2020-03-03               South West      0
## 586  2020-03-04               South West      0
## 587  2020-03-05               South West      0
## 588  2020-03-06               South West      0
## 589  2020-03-07               South West      0
## 590  2020-03-08               South West      0
## 591  2020-03-09               South West      0
## 592  2020-03-10               South West      0
## 593  2020-03-11               South West      1
## 594  2020-03-12               South West      0
## 595  2020-03-13               South West      0
## 596  2020-03-14               South West      1
## 597  2020-03-15               South West      0
## 598  2020-03-16               South West      0
## 599  2020-03-17               South West      2
## 600  2020-03-18               South West      2
## 601  2020-03-19               South West      5
## 602  2020-03-20               South West      3
## 603  2020-03-21               South West      6
## 604  2020-03-22               South West      9
## 605  2020-03-23               South West      9
## 606  2020-03-24               South West      7
## 607  2020-03-25               South West      9
## 608  2020-03-26               South West     11
## 609  2020-03-27               South West     13
## 610  2020-03-28               South West     21
## 611  2020-03-29               South West     18
## 612  2020-03-30               South West     23
## 613  2020-03-31               South West     23
## 614  2020-04-01               South West     22
## 615  2020-04-02               South West     23
## 616  2020-04-03               South West     30
## 617  2020-04-04               South West     42
## 618  2020-04-05               South West     32
## 619  2020-04-06               South West     34
## 620  2020-04-07               South West     39
## 621  2020-04-08               South West     47
## 622  2020-04-09               South West     24
## 623  2020-04-10               South West     46
## 624  2020-04-11               South West     43
## 625  2020-04-12               South West     23
## 626  2020-04-13               South West     27
## 627  2020-04-14               South West     24
## 628  2020-04-15               South West     32
## 629  2020-04-16               South West     29
## 630  2020-04-17               South West     33
## 631  2020-04-18               South West     25
## 632  2020-04-19               South West     31
## 633  2020-04-20               South West     26
## 634  2020-04-21               South West     26
## 635  2020-04-22               South West     22
## 636  2020-04-23               South West     17
## 637  2020-04-24               South West     19
## 638  2020-04-25               South West     15
## 639  2020-04-26               South West     27
## 640  2020-04-27               South West     13
## 641  2020-04-28               South West     17
## 642  2020-04-29               South West     15
## 643  2020-04-30               South West     26
## 644  2020-05-01               South West      6
## 645  2020-05-02               South West      7
## 646  2020-05-03               South West     10
## 647  2020-05-04               South West     16
## 648  2020-05-05               South West     14
## 649  2020-05-06               South West     18
## 650  2020-05-07               South West     16
## 651  2020-05-08               South West      6
## 652  2020-05-09               South West     11
## 653  2020-05-10               South West      5
## 654  2020-05-11               South West      8
## 655  2020-05-12               South West      7
## 656  2020-05-13               South West      7
## 657  2020-05-14               South West      6
## 658  2020-05-15               South West      4
## 659  2020-05-16               South West      4
## 660  2020-05-17               South West      6
## 661  2020-05-18               South West      4
## 662  2020-05-19               South West      6
## 663  2020-05-20               South West      1
## 664  2020-05-21               South West      9
## 665  2020-05-22               South West      6
## 666  2020-05-23               South West      6
## 667  2020-05-24               South West      3
## 668  2020-05-25               South West      7
## 669  2020-05-26               South West     11
## 670  2020-05-27               South West      5
## 671  2020-05-28               South West      8
## 672  2020-05-29               South West      4
## 673  2020-05-30               South West      3
## 674  2020-05-31               South West      2
## 675  2020-06-01               South West      6
## 676  2020-06-02               South West      2
## 677  2020-06-03               South West      5
## 678  2020-06-04               South West      1
## 679  2020-06-05               South West      0

1.5 Completion date

We extract the completion date from the NHS Pathways file timestamp:


database_date <- attr(x, "timestamp")
database_date
## [1] "2020-06-04"

The completion date of the NHS Pathways data is Thursday 04 Jun 2020.

1.6 Auxiliary functions

These are functions which will be used further in the analyses.

Function to estimate the generalised R-squared as the proportion of deviance explained by a given model:


## Function to calculate R2 for Poisson model
## not adjusted for model complexity but all models have the same DF here

Rsq <- function(x) {
  1 - (x$deviance / x$null.deviance)
}

Function to extract growth rates per region as well as halving times, and the associated 95% confidence intervals:


## function to extract the coefficients, find the level of the intercept,
## reconstruct the values of r, get confidence intervals

get_r <- function(model) {
  ##  extract coefficients and conf int
  out <- data.frame(r = coef(model))  %>%
    rownames_to_column("var") %>% 
    cbind(confint(model)) %>%
    filter(!grepl("day_of_week", var)) %>% 
    filter(grepl("day", var)) %>%
    rename(lower_95 = "2.5 %",
           upper_95 = "97.5 %") %>%
    mutate(var = sub("day:", "", var))
  
  ## reconstruct values: intercept + region-coefficient
  for (i in 2:nrow(out)) {
    out[i, -1] <- out[1, -1] + out[i, -1]
  }
  
  ## find the name of the intercept, restore regions names
  out <- out %>%
    mutate(nhs_region = model$xlevels$nhs_region) %>%
    select(nhs_region, everything(), -var)
  
  ## find halving times
  halving <- log(0.5) / out[,-1] %>%
    rename(halving_t = r,
           halving_t_lower_95 = lower_95,
           halving_t_upper_95 = upper_95)
  
  ## set halving times with exclusion intervals to NA
  no_halving <- out$lower_95 < 0 & out$upper_95 > 0
  halving[no_halving, ] <- NA_real_
  
  ## return all data
  cbind(out, halving)
  
}

Functions used in the correlation analysis between NHS Pathways reports and deaths:

## Function to calculate Pearson's correlation between deaths and lagged
## reports. Note that `pearson` can be replaced with `spearman` for rank
## correlation.

getcor <- function(x, ndx) {
  return(cor(x$deaths[ndx],
             x$note_lag[ndx],
             use = "complete.obs",
             method = "pearson"))
}

## Catch if sample size throws an error
getcor2 <- possibly(getcor, otherwise = NA)

getboot <- function(x) {
  result <- boot::boot.ci(boot::boot(x, getcor2, R = 1000), 
                           type = "bca")
  return(data.frame(n = sum(!is.na(x$note_lag) & !is.na(x$deaths)),
                    r = result$t0,
                    r_low = result$bca[4],
                    r_hi = result$bca[5]))
}

Function to classify the day of the week into weekend, Monday, and the rest:


## Fn to add day of week
day_of_week <- function(df) {
  df %>% 
    dplyr::mutate(day_of_week = lubridate::wday(date, label = TRUE)) %>% 
    dplyr::mutate(day_of_week = dplyr::case_when(
      day_of_week %in% c("Sat", "Sun") ~ "weekend",
      day_of_week %in% c("Mon") ~ "monday",
      !(day_of_week %in% c("Sat", "Sun", "Mon")) ~ "rest_of_week"
    ) %>% 
      factor(levels = c("rest_of_week", "monday", "weekend")))
}

Custom color palettes, color scales, and vectors of colors:


pal <- c("#006212",
         "#ae3cab",
         "#00db90",
         "#960c00",
         "#55aaff",
         "#ff7e78",
         "#00388d")

age.pal <- viridis::viridis(3,begin = 0.1, end = 0.7)

3 Comparison with deaths time series

3.1 Outline

We want to explore the correlation between NHS Pathways reports and deaths, and assess the potential for reports to be used as an early warning system for disease resurgence.

Death data are publically available. We truncate the time series to avoid bias from reporting delay - we assume a conservative delay of three weeks.

3.2 Lagged correlation

We calculate Pearson’s correlation coefficient between deaths and NHS Pathways notifications using different lags. Confidence intervals are obtained using bootstrap. Note that results were also confirmed using Spearman’s rank correlation.

First we join the NHS Pathways and death data, and aggregate over all England:

## truncate death data for reporting delay
trunc_date <- max(dth$date_report) - delay_max

dth_trunc <- dth %>%
  rename(date = date_report) %>%
  filter(date <= trunc_date) 

## join with notification data
all_data <- x %>% 
  filter(!is.na(nhs_region)) %>%
  group_by(date, nhs_region) %>%
  summarise(count = sum(count, na.rm = T)) %>%
  ungroup %>%
  inner_join(dth_trunc,
             by = c("date","nhs_region"))

all_tot <- all_data %>%
  group_by(date) %>%
  summarise(count = sum(count, na.rm = TRUE),
            deaths = sum(deaths, na.rm = TRUE)) 

We calculate correlation with lagged NHS Pathways reports from 0 to 30 days behind deaths:


## Calculate all correlations + bootstrap CIs
lag_cor <- data.frame()
for (i in 0:30) {
  
  ## lag reports
  summary <- all_tot %>% 
    mutate(note_lag = lag(count, i)) %>%
    ## calculate rank correlation and bootstrap CI
    getboot(.) %>%
    mutate(lag = i)

  lag_cor <- bind_rows(lag_cor, summary)
}

cor_vs_lag <- ggplot(lag_cor, aes(lag, r)) +
  theme_bw() +
  geom_ribbon(aes(ymin = r_low, ymax = r_hi), alpha = 0.2) +
  geom_hline(yintercept = 0, lty = "longdash") +
  geom_point() +
  geom_line() +
  labs(x = "Lag between NHS pathways and death data (days)",
       y = "Pearson's correlation") +
  large_txt
cor_vs_lag


l_opt <- which.max(lag_cor$r)

This analysis suggests that the best lag is 23 days. We then compare and plot the number of deaths reported against the number of NHS Pathways reports lagged by 23 days.


all_tot <- all_tot %>%
  rename(date_death = date) %>%
  mutate(note_lag = lag(count, lag_cor$lag[l_opt]),
         note_lag_c = (note_lag - mean(note_lag, na.rm = T)),
         date_note = lag(date_death,16))

lag_mod <- glm(deaths ~ note_lag, data = all_tot, family = "quasipoisson")

summary(lag_mod)
## 
## Call:
## glm(formula = deaths ~ note_lag, family = "quasipoisson", data = all_tot)
## 
## Deviance Residuals: 
##     Min       1Q   Median       3Q      Max  
## -7.4664  -1.9189   0.4157   1.6759   4.6120  
## 
## Coefficients:
##              Estimate Std. Error t value Pr(>|t|)    
## (Intercept) 5.141e+00  5.135e-02   100.1   <2e-16 ***
## note_lag    1.005e-05  4.880e-07    20.6   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for quasipoisson family taken to be 6.98988)
## 
##     Null deviance: 3237.34  on 35  degrees of freedom
## Residual deviance:  243.75  on 34  degrees of freedom
##   (23 observations deleted due to missingness)
## AIC: NA
## 
## Number of Fisher Scoring iterations: 4

exp(coefficients(lag_mod))
## (Intercept)    note_lag 
##   170.85662     1.00001
exp(confint(lag_mod))
##                  2.5 %     97.5 %
## (Intercept) 154.352174 188.772235
## note_lag      1.000009   1.000011

Rsq(lag_mod)
## [1] 0.9247065

mod_fit <- as.data.frame(predict(lag_mod, type = "link", se.fit = TRUE)[1:2])

all_tot_pred <- 
  all_tot %>%
  filter(!is.na(note_lag)) %>%
  mutate(pred = mod_fit$fit,
         pred.se = mod_fit$se.fit,
         low = exp(pred - 1.96*pred.se),
         hi = exp(pred + 1.96*pred.se))


glm_fit <- all_tot_pred %>% 
    filter(!is.na(note_lag)) %>%
  ggplot(aes(x = note_lag, y = deaths)) +
  geom_point() + 
  geom_line(aes(y = exp(pred))) + 
  geom_ribbon(aes(ymin = low, ymax = hi), alpha = 0.3, col = "grey") +
  theme_bw() +
  labs(y = "Daily number of\ndeaths reported",
       x = "Daily number of NHS Pathways reports") +
  large_txt

glm_fit

4 Supplementary figures

4.1 Serial interval distribution

This is a comparison of gamma versus lognormal distribution for the serial interval used to convert r to R in our analysis. Both distributions are parameterised with mean 4.7 and standard deviation 2.9.

SI_param <- epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
                                        shape = SI_param$shape,
                                        scale = SI_param$scale, w = 0.5)

SI_distribution2 <- distcrete::distcrete("lnorm", interval = 1,
                                        meanlog = log(4.7),
                                        sdlog = log(2.9), w = 0.5)

SI_dist1 <- data.frame(x = SI_distribution$r(1e5)) 
SI_dist1 <- count(SI_dist1, x) %>%
    ggplot() +
    geom_col(aes(x = x, y = n)) +
    labs(x = "Serial interval (days)", y = "Frequency") +
    scale_x_continuous(breaks = seq(0, 30, 5)) +
    theme_bw()

SI_dist2 <- data.frame(x = SI_distribution2$r(1e5)) 
SI_dist2 <- count(SI_dist2, x) %>%
    ggplot() +
    geom_col(aes(x = x, y = n)) +
    labs(x = "Serial interval (days)", y = "Frequency") +
    scale_x_continuous(breaks = seq(0, 200, 20), limits = c(0, 200)) +
    theme_bw()


ggpubr::ggarrange(SI_dist1,
                  SI_dist2,
                  nrow = 1,
                  labels = "AUTO") 

4.2 Sensitivity analysis - 7 or 21 days moving window

We reproduce the window analysis with either a 7 or 21 days window for sensitivity purposes.

First with the 7 days window:

## set moving time window (1/2/3 weeks)
w <- 7

# create empty df
r_all_sliding_7days <- NULL

## make data for model
x_model_all_moving <- x %>%
  filter(!is.na(nhs_region)) %>% 
  group_by(date, nhs_region) %>%
  summarise(n = sum(count)) 

unique_dates <- unique(x_model_all_moving$date)

for (i in 1:(length(unique_dates) - w)) {
  
  date_i <- unique_dates[i]
  
  date_i_max <- date_i + w
  
  model_data <- x_model_all_moving %>%
    filter(date >= date_i & date < date_i_max) %>%
    mutate(day = as.integer(date - date_i)) %>% 
    day_of_week()
  
  
  mod <- glm(n ~ day * nhs_region + day_of_week,
             data = model_data,
             family = 'quasipoisson')
  
  # get growth rate
  r <- get_r(mod)
  r$w_min <- date_i
  r$w_max <- date_i_max
  
  # combine all estimates
  r_all_sliding_7days <- bind_rows(r_all_sliding_7days, r)
  
}

#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
                                        shape = SI_param$shape,
                                        scale = SI_param$scale,
                                        w = 0.5)

#convert growth rates r to R0
r_all_sliding_7days <- r_all_sliding_7days %>%
  mutate(R = epitrix::r2R0(r, SI_distribution),
         R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
         R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))
# plot
plot_growth <-
  r_all_sliding_7days %>%
  ggplot(aes(x = w_max, y = r)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated daily growth rate (r)") +
  scale_colour_manual(values = pal)
plot_R <- r_all_sliding_7days %>%
  ggplot(aes(x = w_max, y = R)) +
  geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 1, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated effective reproduction\nnumber (Re)") +
  scale_colour_manual(values = pal)

R <- r_all_sliding_7days %>%
  mutate(lower_95 = R_lower_95, 
         upper_95 = R_upper_95,
         value = R,
         measure = "R",
         reference = 1)

r_R <- r_all_sliding_7days %>%
  mutate(measure = "r",
         value = r,
         reference = 0) %>%
  bind_rows(R)

r_R_7 <- r_R %>%
  ggplot(aes(x = w_max, y = value)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(aes(yintercept = reference), linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0,0, "cm"),
        strip.background = element_blank(),
        strip.placement = "outside"
  ) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "", y = "") +
  scale_colour_manual(values = pal) +
  facet_grid(rows = vars(measure),
             scales = "free_y",
             switch = "y",
             labeller = as_labeller(c(r = "Daily growth rate (r)",
                                      R = "Effective reproduction\nnumber (Re)")))

Then with the 21 days window:

## set moving time window (1/2/3 weeks)
w <- 21

# create empty df
r_all_sliding_21days <- NULL

## make data for model
x_model_all_moving <- x %>%
  filter(!is.na(nhs_region)) %>% 
  group_by(date, nhs_region) %>%
  summarise(n = sum(count)) 

unique_dates <- unique(x_model_all_moving$date)

for (i in 1:(length(unique_dates) - w)) {
  
  date_i <- unique_dates[i]
  
  date_i_max <- date_i + w
  
  model_data <- x_model_all_moving %>%
    filter(date >= date_i & date < date_i_max) %>%
    mutate(day = as.integer(date - date_i)) %>% 
    day_of_week()
  
  
  mod <- glm(n ~ day * nhs_region + day_of_week,
             data = model_data,
             family = 'quasipoisson')
  
  # get growth rate
  r <- get_r(mod)
  r$w_min <- date_i
  r$w_max <- date_i_max
  
  # combine all estimates
  r_all_sliding_21days <- bind_rows(r_all_sliding_21days, r)
  
}

#serial interval distribution
SI_param = epitrix::gamma_mucv2shapescale(4.7, 2.9/4.7)
SI_distribution <- distcrete::distcrete("gamma", interval = 1,
                                        shape = SI_param$shape,
                                        scale = SI_param$scale,
                                        w = 0.5)

#convert growth rates r to R0
r_all_sliding_21days <- r_all_sliding_21days %>%
  mutate(R = epitrix::r2R0(r, SI_distribution),
         R_lower_95 = epitrix::r2R0(lower_95, SI_distribution),
         R_upper_95 = epitrix::r2R0(upper_95, SI_distribution))
# plot
plot_growth <-
  r_all_sliding_21days %>%
  ggplot(aes(x = w_max, y = r)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(colour = guide_legend(title = "",override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated daily growth rate (r)") +
  scale_colour_manual(values = pal)
# plot
plot_R <-
  r_all_sliding_21days %>%
  ggplot(aes(x = w_max, y = R)) +
  geom_ribbon(aes(ymin = R_lower_95, ymax = R_upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(yintercept = 1, linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0.5,0.5, "cm")) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "",
       y = "Estimated effective reproduction\nnumber (Re)") +
  scale_colour_manual(values = pal)

R <- r_all_sliding_21days %>%
  mutate(lower_95 = R_lower_95, 
         upper_95 = R_upper_95,
         value = R,
         measure = "R",
         reference = 1)

r_R <- r_all_sliding_21days %>%
  mutate(measure = "r",
         value = r,
         reference = 0) %>%
  bind_rows(R)

r_R_21 <- r_R %>%
  ggplot(aes(x = w_max, y = value)) +
  geom_ribbon(aes(ymin = lower_95, ymax = upper_95, fill = nhs_region), alpha = 0.1) +
  geom_line(aes(colour = nhs_region)) +
  geom_point(aes(colour = nhs_region)) +
  geom_hline(aes(yintercept = reference), linetype = "dashed") +
  theme_bw() +
  scale_weeks +
  theme(legend.position = "bottom",
        plot.margin = margin(0.5,1,0,0, "cm"),
        strip.background = element_blank(),
        strip.placement = "outside"
  ) +
  guides(color = guide_legend(title = "", override.aes = list(fill = NA)), fill = FALSE) +
  labs(x = "", y = "") +
  scale_colour_manual(values = pal) +
  facet_grid(rows = vars(measure),
             scales = "free_y",
             switch = "y",
             labeller = as_labeller(c(r = "Daily growth rate (r)",
                                      R = "Effective reproduction\nnumber (Re)")))

And we combine both outputs into a single plot:


ggpubr::ggarrange(r_R_7,
                  r_R_21,
                  nrow = 2,
                  labels = "AUTO",
                  common.legend = TRUE,
                  legend = "bottom") 

4.3 Correlation between NHS Pathways reports and deaths by NHS region


lag_cor_reg <- data.frame()

for (i in 0:30) {

  summary <-
    all_data %>%
    group_by(nhs_region) %>%
    mutate(note_lag = lag(count, i)) %>%
    ## calculate rank correlation and bootstrap CI for each region
    group_modify(~getboot(.x)) %>%
    mutate(lag = i)
  
  lag_cor_reg <- bind_rows(lag_cor_reg, summary)
}

cor_vs_lag_reg <- 
lag_cor_reg %>%
ggplot(aes(lag, r, col = nhs_region)) +
  geom_hline(yintercept = 0, lty = "longdash") +
  geom_ribbon(aes(ymin = r_low, ymax = r_hi, col = NULL, fill = nhs_region), alpha = 0.2) +
  geom_point() +
  geom_line() +
  facet_wrap(~nhs_region) +
  scale_color_manual(values = pal) +
  scale_fill_manual(values = pal, guide = F) +  
  theme_bw() +
  labs(x = "Lag between NHS pathways and death data (days)", y = "Pearson's correlation", col = "NHS region") +
  theme(legend.position = "bottom") +
  guides(color = guide_legend(override.aes = list(fill = NA)))

cor_vs_lag_reg

5 Export data

We save the tables created during our analysis:


if (!dir.exists("excel_tables")) {
  dir.create("excel_tables")
}


## list all tables, and loop over export
tables_to_export <- c("r_all_sliding", "lag_cor")

for (e in tables_to_export) {
  rio::export(get(e),
              file.path("excel_tables",
                        paste0(e, ".xlsx")))
}

## also export result from regression on lagged data 
rio::export(lag_mod, file.path("excel_tables", "lag_mod.rds"))

6 System information

6.1 Outline

The following information documents the system on which the document was compiled.

6.2 System

This provides information on the operating system.

Sys.info()
##                                                                                            sysname 
##                                                                                           "Darwin" 
##                                                                                            release 
##                                                                                           "19.5.0" 
##                                                                                            version 
## "Darwin Kernel Version 19.5.0: Thu Apr 30 18:25:59 PDT 2020; root:xnu-6153.121.1~7/RELEASE_X86_64" 
##                                                                                           nodename 
##                                                                                   "Mac-1503.local" 
##                                                                                            machine 
##                                                                                           "x86_64" 
##                                                                                              login 
##                                                                                             "root" 
##                                                                                               user 
##                                                                                           "runner" 
##                                                                                     effective_user 
##                                                                                           "runner"

6.3 R environment

This provides information on the version of R used:

R.version
##                _                           
## platform       x86_64-apple-darwin15.6.0   
## arch           x86_64                      
## os             darwin15.6.0                
## system         x86_64, darwin15.6.0        
## status                                     
## major          3                           
## minor          6.3                         
## year           2020                        
## month          02                          
## day            29                          
## svn rev        77875                       
## language       R                           
## version.string R version 3.6.3 (2020-02-29)
## nickname       Holding the Windsock

6.4 R packages

This provides information on the packages used:

sessionInfo()
## R version 3.6.3 (2020-02-29)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS Catalina 10.15.5
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] ggnewscale_0.4.1     ggpubr_0.3.0         lubridate_1.7.8     
##  [4] chngpt_2020.5-21     cyphr_1.1.0          DT_0.13             
##  [7] kableExtra_1.1.0     janitor_2.0.1        remotes_2.1.1       
## [10] projections_0.4.1    earlyR_0.0.1         epitrix_0.2.2       
## [13] distcrete_1.0.3      incidence_1.7.1      rio_0.5.16          
## [16] reshape2_1.4.4       rvest_0.3.5          xml2_1.3.2          
## [19] linelist_0.0.40.9000 forcats_0.5.0        stringr_1.4.0       
## [22] dplyr_1.0.0          purrr_0.3.4          readr_1.3.1         
## [25] tidyr_1.1.0          tibble_3.0.1         ggplot2_3.3.1       
## [28] tidyverse_1.3.0      here_0.1             reportfactory_0.0.5 
## 
## loaded via a namespace (and not attached):
##  [1] colorspace_1.4-1  selectr_0.4-2     ggsignif_0.6.0    ellipsis_0.3.1   
##  [5] rprojroot_1.3-2   snakecase_0.11.0  fs_1.4.1          rstudioapi_0.11  
##  [9] farver_2.0.3      fansi_0.4.1       splines_3.6.3     knitr_1.28       
## [13] jsonlite_1.6.1    broom_0.5.6       dbplyr_1.4.4      compiler_3.6.3   
## [17] httr_1.4.1        backports_1.1.7   assertthat_0.2.1  Matrix_1.2-18    
## [21] cli_2.0.2         htmltools_0.4.0   prettyunits_1.1.1 tools_3.6.3      
## [25] gtable_0.3.0      glue_1.4.1        Rcpp_1.0.4.6      carData_3.0-4    
## [29] cellranger_1.1.0  vctrs_0.3.1       nlme_3.1-144      matchmaker_0.1.1 
## [33] crosstalk_1.1.0.1 xfun_0.14         ps_1.3.3          openxlsx_4.1.5   
## [37] lifecycle_0.2.0   rstatix_0.5.0     MASS_7.3-51.5     scales_1.1.1     
## [41] hms_0.5.3         sodium_1.1        yaml_2.2.1        curl_4.3         
## [45] gridExtra_2.3     stringi_1.4.6     kyotil_2019.11-22 boot_1.3-24      
## [49] pkgbuild_1.0.8    zip_2.0.4         rlang_0.4.6       pkgconfig_2.0.3  
## [53] evaluate_0.14     lattice_0.20-38   labeling_0.3      htmlwidgets_1.5.1
## [57] cowplot_1.0.0     processx_3.4.2    tidyselect_1.1.0  plyr_1.8.6       
## [61] magrittr_1.5      R6_2.4.1          generics_0.0.2    DBI_1.1.0        
## [65] pillar_1.4.4      haven_2.3.1       foreign_0.8-75    withr_2.2.0      
## [69] mgcv_1.8-31       survival_3.1-8    abind_1.4-5       modelr_0.1.8     
## [73] crayon_1.3.4      car_3.0-8         utf8_1.1.4        rmarkdown_2.2    
## [77] viridis_0.5.1     grid_3.6.3        readxl_1.3.1      data.table_1.12.8
## [81] blob_1.2.1        callr_3.4.3       reprex_0.3.0      digest_0.6.25    
## [85] webshot_0.5.2     munsell_0.5.0     viridisLite_0.3.0